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Object Recognition

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하위 태스크: 3
평가표: 9
보존된 평가 기록: 31

Object recognition is a computer vision technique for detecting + classifying objects in images or videos. Since this is a combined task of object detection plus image classification, the state-of-the-art tables are recorded for each component task here and here.

<span style="color:grey; opacity: 0.6">( Image credit: Tensorflow Object Detection API )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/TsukamotoShuchi/ROCK

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Recognizing Object by Components with Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural Networks

https://github.com/Varat7v2/adaptive-threshold

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Adaptive Threshold for Online Object Recognition and Re-identification Tasks

https://github.com/Vision-CAIR/RelTransformer

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RelTransformer: A Transformer-Based Long-Tail Visual Relationship Recognition

https://github.com/VoyagerXvoyagerx/InstructSAM

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InstructSAM: A Training-Free Framework for Instruction-Oriented Remote Sensing Object Recognition

https://github.com/WXinlong/ForeSeE

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Task-Aware Monocular Depth Estimation for 3D Object Detection

https://github.com/Wuziyi616/EventCLIP

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EventCLIP: Adapting CLIP for Event-based Object Recognition

https://github.com/Xiangyu-Gao/Radar_multiple_perspective_object_detection

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RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition

https://github.com/Xiangyu-Gao/mmWave-radar-signal-processing-and-microDoppler-classification

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Experiments with mmWave Automotive Radar Test-bed

https://github.com/YeLyuUT/MSDNet

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UAVid: A Semantic Segmentation Dataset for UAV Imagery

https://github.com/ZF1044404254/MMAL-Net

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Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization

https://github.com/ZF1044404254/TBMSL-Net

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Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization

https://github.com/ZilinGao/Global-Second-order-Pooling-Convolutional-Networks

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Global Second-order Pooling Convolutional Networks

https://github.com/abheesht17/super-pixels

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Superpixel-based Knowledge Infusion in Deep Neural Networks for Image Classification

https://github.com/acaglayan/CNN_randRNN

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When CNNs Meet Random RNNs: Towards Multi-Level Analysis for RGB-D Object and Scene Recognition

https://github.com/acecreamu/color-constancy-googlenet

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Artificial Color Constancy via GoogLeNet with Angular Loss Function

https://github.com/addisonwang2013/evdistill

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EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation

https://github.com/aditya-vora/unsupervised_patch_extraction

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A Classification approach towards Unsupervised Learning of Visual Representations

https://github.com/ahnchive/recon-mnist

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Reconstruction-guided attention improves the robustness and shape processing of neural networks

https://github.com/ajayjohnalex/bee_detection

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Enhancing Pollinator Conservation towards Agriculture 4.0: Monitoring of Bees through Object Recognition

https://github.com/ajaysub110/satbench

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SATBench: Benchmarking the speed-accuracy tradeoff in object recognition by humans and dynamic neural networks

https://github.com/ali2500/burst-benchmark

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BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Video

https://github.com/aliayub7/cl_hri

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Continual Learning through Human-Robot Interaction: Human Perceptions of a Continual Learning Robot in Repeated Interactions

https://github.com/aliborji/DeetctionUpperbound

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Empirical Upper Bound, Error Diagnosis and Invariance Analysis of Modern Object Detectors

https://github.com/aliborji/ObjectNetReanalysis

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ObjectNet Dataset: Reanalysis and Correction

https://github.com/aliborji/coco_oi

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Complementary datasets to COCO for object detection

https://github.com/aliborji/d2o

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Diverse, Difficult, and Odd Instances (D2O): A New Test Set for Object Classification

https://github.com/alinlab/OAMixer

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OAMixer: Object-aware Mixing Layer for Vision Transformers

https://github.com/alinlab/remixer

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OAMixer: Object-aware Mixing Layer for Vision Transformers

https://github.com/allenai/visual-reasoning-rationalization

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Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs

https://github.com/amik-tj/dynamic_rectification_knowledge_distillation

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Dynamic Rectification Knowledge Distillation

https://github.com/amosy3/MUST

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Teacher-Student Consistency For Multi-Source Domain Adaptation

https://github.com/anaerovane/lrsaa

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Large-scale Remote Sensing Image Target Recognition and Automatic Annotation

https://github.com/anshuln/CogCNN

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Teaching CNNs to mimic Human Visual Cognitive Process & regularise Texture-Shape bias

https://github.com/arekavandi/transformer-sod

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Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-Art

https://github.com/arshiahemmat/illusionbench

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Hidden in Plain Sight: Evaluating Abstract Shape Recognition in Vision-Language Models

https://github.com/artipago/Role_of_Feedback_in_Predictive_Coding

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On the role of feedback in visual processing: a predictive coding perspective

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